Physics and chemistry from parsimonious representations: image analysis via invariant variational autoencoders

Electron, optical, and scanning probe microscopy methods are generating ever increasing volume of image data containing information on atomic and mesoscale structures and functionalities. This necessitates the development of the machine learning methods for discovery of physical and chemical phenome...

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Bibliographic Details
Published in:	npj computational materials Vol. 10; no. 1; pp. 183 - 19
Main Authors:	Valleti, Mani, Ziatdinov, Maxim, Liu, Yongtao, Kalinin, Sergei V.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 14.08.2024 Nature Publishing Group Nature Portfolio
Subjects:	639/301 639/925/918 Broken symmetry Characterization and Evaluation of Materials Chemistry and Materials Science Computational Intelligence Data analysis Datasets graphene Image analysis Image processing Invariants Machine learning MATERIALS SCIENCE Mathematical and Computational Engineering Mathematical and Computational Physics Mathematical Modeling and Industrial Mathematics Microscopy Nanoparticles Representations Scanning probe microscopy Scanning transmission electron microscopy Scanning tunneling microscopy Theoretical Transmission electron microscopy
ISSN:	2057-3960, 2057-3960
Online Access:	Get full text
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